期刊
ANNALS OF STATISTICS
卷 37, 期 4, 页码 1705-1732出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/08-AOS620
关键词
Linear models; model selection; nonparametric statistics
We show that, under a sparsity scenario, the Lasso estimator and the Dantzig selector exhibit similar behavior. Forboth methods, wederive, inparallel, oracle inequalities for the prediction risk in the general nonparametric regression model, as well as bounds on the e(p) estimation loss for 1 <= p <= 2 in the linear model when the number of variables can be Much larger than the sample size.
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